Approximate nearest neighbor (ANN) configurations directly impact RAG latency and answer quality by balancing the trade-offs between retrieval speed and accuracy. When ANN settings prioritize speed—such as reducing the number of search probes, using fewer index trees, or limiting cluster exploration—the retrieval phase becomes faster, reducing end-to-end latency. For example, in HNSW-based systems, lowering the efSearch parameter limits how many nodes are explored during a query, speeding up searches. Similarly, in FAISS’s IVF index, decreasing nprobe (the number of clusters searched) reduces computation but skips potentially relevant vectors. These optimizations make RAG systems more responsive, especially for large datasets where exact searches are impractical. However, faster retrieval risks missing critical context, which can degrade answer quality.
The accuracy of retrieved documents directly affects answer quality. If ANN settings sacrifice too much precision, the generator might receive irrelevant or incomplete context. For instance, if a user asks about “treatment for bacterial infections” and the ANN retrieves documents about viruses due to hasty indexing, the generator could produce a misleading answer. Conversely, higher-accuracy settings—like increasing HNSW’s efSearch or FAISS’s nprobe—improve retrieval relevance by exploring more candidate vectors, giving the generator better context. This is critical for nuanced queries requiring domain-specific knowledge. However, overly aggressive accuracy settings (e.g., near-exhaustive searches) can inflate latency without proportional quality gains, especially if the generator can synthesize useful answers from partially relevant context.
The optimal ANN configuration depends on the RAG system’s priorities. For latency-sensitive applications (e.g., chatbots), faster ANN settings with moderate accuracy may suffice, especially if the generator is robust to noise. For accuracy-critical use cases (e.g., medical advice), slower but precise retrieval is justified. Tools like FAISS or Annoy allow tuning parameters (e.g., n_trees, quantization) to strike this balance. Testing is key: measuring how different efSearch or nprobe values affect both retrieval time and answer quality (via metrics like precision@k or human evaluation) helps identify the best trade-off. For example, a system might achieve 90% of the answer quality with 50% faster retrieval by optimizing these parameters, making the latency-quality trade-off manageable.